


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'ClapFeatureExtractor'
    __magic_name__         =						('RobertaTokenizer', 'RobertaTokenizerFast')
    def __init__(			self     ,   __snake_case     ,   __snake_case       ):
         super().__init__(__snake_case     ,   __snake_case       )
    def __call__(			self     ,   __snake_case=None     ,   __snake_case=None     ,   __snake_case=None     ,   **__snake_case       ):
         snake_case											=							kwargs.pop('''sampling_rate'''     ,   __snake_case       )

         if text is None and audios is None:
              raise ValueError('''You have to specify either text or audios. Both cannot be none.'''       )

         if text is not None:
              snake_case											=							self.tokenizer(__snake_case     ,   return_tensors=__snake_case     ,   **__snake_case       )

         if audios is not None:
              snake_case											=							self.feature_extractor(
                  __snake_case     ,   sampling_rate=__snake_case     ,   return_tensors=__snake_case     ,   **__snake_case       )

         if text is not None and audios is not None:
              snake_case											=							audio_features.input_features
              return encoding
         elif text is not None:
              return encoding
         else:
              return BatchEncoding(data=dict(**__snake_case       )     ,   tensor_type=__snake_case       )
    def 						a_     (			self     ,   *__snake_case     ,   **__snake_case       ):
         return self.tokenizer.batch_decode(*__snake_case     ,   **__snake_case       )
    def 						a_     (			self     ,   *__snake_case     ,   **__snake_case       ):
         return self.tokenizer.decode(*__snake_case     ,   **__snake_case       )




    @property
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer.model_input_names
         snake_case											=							self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names       )       )








class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         snake_case											=							n
         snake_case											=							[None] * self.n
         snake_case											=							0  # index of the first element
         snake_case											=							0
         snake_case											=							0
    def __len__(			self       ):
         return self.size
    def 						a_     (			self       ):
         return self.size == 0
    def 						a_     (			self       ):
         return False if self.is_empty() else self.array[self.front]
    def 						a_     (			self     ,   __snake_case       ):
         if self.size >= self.n:
              raise Exception('''QUEUE IS FULL'''       )

         snake_case											=							data
         snake_case											=							(self.rear + 1) % self.n
         self.size += 1
         return self




    def 						a_     (			self       ):
         if self.size == 0:
              raise Exception('''UNDERFLOW'''       )

         snake_case											=							self.array[self.front]
         snake_case											=							None
         snake_case											=							(self.front + 1) % self.n
         self.size -= 1
         return temp




